Overview

Dataset statistics

Number of variables13
Number of observations310
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.6 KiB
Average record size in memory104.4 B

Variable types

NUM12
CAT1

Reproduction

Analysis started2020-08-01 08:12:07.447707
Analysis finished2020-08-01 08:13:22.395089
Duration1 minute and 14.95 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

pelvic_incidence has unique values Unique
pelvic tilt has unique values Unique
pelvic_radius has unique values Unique
degree_spondylolisthesis has unique values Unique
pelvic_slope has unique values Unique
Direct_tilt has unique values Unique
thoracic_slope has unique values Unique
cervical_tilt has unique values Unique
sacrum_angle has unique values Unique
scoliosis_slope has unique values Unique

Variables

pelvic_incidence
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.49665292951613
Minimum26.14792141
Maximum129.8340406
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:22.666707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum26.14792141
5-th percentile35.9892215
Q146.43029421
median58.69103813
Q372.87769551
95-th percentile87.86906338
Maximum129.8340406
Range103.6861192
Interquartile range (IQR)26.4474013

Descriptive statistics

Standard deviation17.23652032
Coefficient of variation (CV)0.2849169249
Kurtosis0.223776658
Mean60.49665293
Median Absolute Deviation (MAD)13.1987684
Skewness0.5204398949
Sum18753.96241
Variance297.0976328
2020-08-01T13:43:22.979207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
58.5216228310.3%
 
50.8192678110.3%
 
42.5172724910.3%
 
72.6438501310.3%
 
59.7261401610.3%
 
46.3902600810.3%
 
86.0412798210.3%
 
48.030623810.3%
 
65.6653469810.3%
 
59.5955403210.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
26.1479214110.3%
 
30.1499363210.3%
 
30.7419381210.3%
 
31.2323873410.3%
 
31.2760118410.3%
 
ValueCountFrequency (%) 
129.834040610.3%
 
118.144654810.3%
 
115.923260610.3%
 
96.6573151110.3%
 
95.4802287310.3%
 

pelvic tilt
Real number (ℝ)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.542821967970966
Minimum-6.554948347000001
Maximum49.4318636
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:23.307311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-6.554948347
5-th percentile3.383367708
Q110.66706906
median16.35768863
Q322.12039474
95-th percentile37.54992764
Maximum49.4318636
Range55.98681195
Interquartile range (IQR)11.45332568

Descriptive statistics

Standard deviation10.00833026
Coefficient of variation (CV)0.5705085691
Kurtosis0.6761753667
Mean17.54282197
Median Absolute Deviation (MAD)5.75742129
Skewness0.676553359
Sum5438.27481
Variance100.1666746
2020-08-01T13:43:23.651064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
24.188884610.3%
 
9.65207487910.3%
 
15.4022125310.3%
 
39.8446687810.3%
 
22.2184820510.3%
 
17.4438376210.3%
 
17.8994017210.3%
 
13.2917897510.3%
 
33.4259512610.3%
 
29.3965454310.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
-6.55494834710.3%
 
-5.84599434110.3%
 
-3.75992987210.3%
 
-2.97002433710.3%
 
-1.32941239810.3%
 
ValueCountFrequency (%) 
49.431863610.3%
 
48.9036526510.3%
 
48.0695309710.3%
 
46.5500531810.3%
 
42.6891951310.3%
 

lumbar_lordosis_angle
Real number (ℝ≥0)

Distinct count280
Unique (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.93092960345161
Minimum14.0
Maximum125.7423855
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:23.994810image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile26.85167104
Q137
median49.56239828
Q362.99999999
95-th percentile85.59549477
Maximum125.7423855
Range111.7423855
Interquartile range (IQR)25.99999999

Descriptive statistics

Standard deviation18.55406396
Coefficient of variation (CV)0.3572834938
Kurtosis0.1618107827
Mean51.9309296
Median Absolute Deviation (MAD)13.1027409
Skewness0.5994514776
Sum16098.58818
Variance344.2532895
2020-08-01T13:43:24.307331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
41.9999999941.3%
 
3541.3%
 
51.9999999941.3%
 
46.9999999941.3%
 
3731.0%
 
57.9999999931.0%
 
3431.0%
 
62.9999999920.6%
 
47.9999999920.6%
 
50.9999999920.6%
 
Other values (270)27990.0%
 
ValueCountFrequency (%) 
1410.3%
 
15.510.3%
 
15.5903634510.3%
 
19.071074610.3%
 
20.030886310.3%
 
ValueCountFrequency (%) 
125.742385510.3%
 
100.744219810.3%
 
96.2830616910.3%
 
95.1576327310.3%
 
93.8927788110.3%
 

sacral_slope
Real number (ℝ≥0)

Distinct count281
Unique (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.95383096141936
Minimum13.3669307
Maximum121.42956559999999
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:24.651061image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum13.3669307
5-th percentile23.48945209
Q133.34712201
median42.40491207
Q352.69588836
95-th percentile63.43494882
Maximum121.4295656
Range108.0626349
Interquartile range (IQR)19.34876634

Descriptive statistics

Standard deviation13.42310216
Coefficient of variation (CV)0.3125006982
Kurtosis3.007434358
Mean42.95383096
Median Absolute Deviation (MAD)9.293570115
Skewness0.7925766942
Sum13315.6876
Variance180.1796717
2020-08-01T13:43:24.947954image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4531.0%
 
56.3099324831.0%
 
35.4170552831.0%
 
33.1113419631.0%
 
34.3803447220.6%
 
55.9228047220.6%
 
33.2152514920.6%
 
29.744881320.6%
 
48.1798301220.6%
 
52.8831393220.6%
 
Other values (271)28692.3%
 
ValueCountFrequency (%) 
13.366930710.3%
 
13.5165681110.3%
 
15.3884678310.3%
 
16.2602047110.3%
 
17.3869721810.3%
 
ValueCountFrequency (%) 
121.429565610.3%
 
79.6951535310.3%
 
78.7940524910.3%
 
78.4078245910.3%
 
77.1957339310.3%
 

pelvic_radius
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.92065502380645
Minimum70.08257486
Maximum163.0710405
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:25.280233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum70.08257486
5-th percentile95.33854069
Q1110.7091963
median118.2681783
Q3125.4676744
95-th percentile139.1360361
Maximum163.0710405
Range92.98846564
Interquartile range (IQR)14.75847813

Descriptive statistics

Standard deviation13.31737704
Coefficient of variation (CV)0.1129350667
Kurtosis0.9346123299
Mean117.920655
Median Absolute Deviation (MAD)7.3969508
Skewness-0.1768348681
Sum36555.40306
Variance177.3525314
2020-08-01T13:43:25.577108image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
141.088149410.3%
 
131.802491410.3%
 
124.646072310.3%
 
128.063620310.3%
 
128.905689210.3%
 
78.9994541110.3%
 
124.115835810.3%
 
122.092953610.3%
 
98.6225116510.3%
 
119.335654610.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
70.0825748610.3%
 
78.9994541110.3%
 
81.024540610.3%
 
82.4560381710.3%
 
84.2414151710.3%
 
ValueCountFrequency (%) 
163.071040510.3%
 
157.84879910.3%
 
151.839856610.3%
 
148.525562410.3%
 
147.894637210.3%
 

degree_spondylolisthesis
Real number (ℝ)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.296694437867743
Minimum-11.05817866
Maximum418.54308210000005
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:25.905213image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-11.05817866
5-th percentile-4.083136048
Q11.603726674
median11.76793377
Q341.28735196
95-th percentile81.69100108
Maximum418.5430821
Range429.6012608
Interquartile range (IQR)39.68362529

Descriptive statistics

Standard deviation37.55902655
Coefficient of variation (CV)1.428279385
Kurtosis38.06870527
Mean26.29669444
Median Absolute Deviation (MAD)14.58141151
Skewness4.317953644
Sum8151.975276
Variance1410.680476
2020-08-01T13:43:26.233359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
49.6720955910.3%
 
67.7273159510.3%
 
5.41582514310.3%
 
5.98855070210.3%
 
1.51720335610.3%
 
-1.53738307410.3%
 
30.3412032710.3%
 
-0.62252664310.3%
 
58.0575415510.3%
 
51.8058992110.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
-11.0581786610.3%
 
-10.6758708310.3%
 
-10.0931081710.3%
 
-9.56924985810.3%
 
-8.94170942110.3%
 
ValueCountFrequency (%) 
418.543082110.3%
 
148.753710910.3%
 
145.378143210.3%
 
124.984405710.3%
 
118.353370110.3%
 

pelvic_slope
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4729792534451613
Minimum0.003220264
Maximum0.9988266840000001
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:26.577107image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.003220264
5-th percentile0.0401541306
Q10.2243670405
median0.475988571
Q30.7048461815
95-th percentile0.9331725621
Maximum0.998826684
Range0.99560642
Interquartile range (IQR)0.480479141

Descriptive statistics

Standard deviation0.2857867408
Coefficient of variation (CV)0.6042268001
Kurtosis-1.156918277
Mean0.4729792534
Median Absolute Deviation (MAD)0.237084432
Skewness0.01623066375
Sum146.6235686
Variance0.08167406123
2020-08-01T13:43:26.905233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.29615236610.3%
 
0.82234464110.3%
 
0.08462126610.3%
 
0.64762586610.3%
 
0.19987498210.3%
 
0.45967421710.3%
 
0.58309801410.3%
 
0.26588949410.3%
 
0.52789143810.3%
 
0.05330099610.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
0.00322026410.3%
 
0.0050453810.3%
 
0.00612831110.3%
 
0.00848556410.3%
 
0.01197107610.3%
 
ValueCountFrequency (%) 
0.99882668410.3%
 
0.99724749110.3%
 
0.99003448110.3%
 
0.98627154710.3%
 
0.98225040810.3%
 

Direct_tilt
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.321526129032257
Minimum7.027
Maximum36.7439
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:27.248964image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7.027
5-th percentile8.551255
Q113.0544
median21.90715
Q328.954075
95-th percentile34.981455
Maximum36.7439
Range29.7169
Interquartile range (IQR)15.899675

Descriptive statistics

Standard deviation8.639423373
Coefficient of variation (CV)0.4051972321
Kurtosis-1.247652588
Mean21.32152613
Median Absolute Deviation (MAD)7.89765
Skewness0.01079554162
Sum6609.6731
Variance74.63963621
2020-08-01T13:43:27.592733image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13.181310.3%
 
33.262810.3%
 
30.28310.3%
 
28.559810.3%
 
14.282610.3%
 
17.900810.3%
 
10.84910.3%
 
23.866510.3%
 
20.466710.3%
 
26.375610.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
7.02710.3%
 
7.055110.3%
 
7.110310.3%
 
7.299410.3%
 
7.317310.3%
 
ValueCountFrequency (%) 
36.743910.3%
 
36.628510.3%
 
36.619410.3%
 
36.357910.3%
 
36.143110.3%
 

thoracic_slope
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.06451129032258
Minimum7.0378
Maximum19.324
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:27.936486image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7.0378
5-th percentile7.592655
Q110.4178
median12.93845
Q315.889525
95-th percentile18.4365
Maximum19.324
Range12.2862
Interquartile range (IQR)5.471725

Descriptive statistics

Standard deviation3.39971285
Coefficient of variation (CV)0.2602250306
Kurtosis-1.087275927
Mean13.06451129
Median Absolute Deviation (MAD)2.6996
Skewness0.02304615982
Sum4049.9985
Variance11.55804746
2020-08-01T13:43:28.264611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7.164610.3%
 
10.224410.3%
 
10.537410.3%
 
11.70410.3%
 
8.849610.3%
 
11.067110.3%
 
11.304210.3%
 
12.843210.3%
 
7.17510.3%
 
12.727410.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
7.037810.3%
 
7.113510.3%
 
7.164610.3%
 
7.17510.3%
 
7.204910.3%
 
ValueCountFrequency (%) 
19.32410.3%
 
19.265910.3%
 
19.205310.3%
 
19.183710.3%
 
19.175610.3%
 

cervical_tilt
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.933316741935485
Minimum7.0306
Maximum16.82108
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:28.622744image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7.0306
5-th percentile7.4306725
Q19.54114
median11.953835
Q314.37181
95-th percentile16.455756
Maximum16.82108
Range9.79048
Interquartile range (IQR)4.83067

Descriptive statistics

Standard deviation2.893265304
Coefficient of variation (CV)0.2424527369
Kurtosis-1.143423797
Mean11.93331674
Median Absolute Deviation (MAD)2.42026
Skewness0.01501718983
Sum3699.32819
Variance8.370984118
2020-08-01T13:43:28.935247image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
15.9225210.3%
 
10.9818910.3%
 
13.8214810.3%
 
9.627510.3%
 
7.2456210.3%
 
13.3366110.3%
 
7.6377110.3%
 
13.2950610.3%
 
8.8912110.3%
 
7.8821210.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
7.030610.3%
 
7.0541110.3%
 
7.0629510.3%
 
7.0874510.3%
 
7.1719710.3%
 
ValueCountFrequency (%) 
16.8210810.3%
 
16.7848610.3%
 
16.7790510.3%
 
16.7406510.3%
 
16.7358110.3%
 

sacrum_angle
Real number (ℝ)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-14.053139487096773
Minimum-35.287375
Maximum6.972071000000001
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:29.278994image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-35.287375
5-th percentile-33.19787365
Q1-24.28952225
median-14.6228555
Q3-3.49709425
95-th percentile5.140722
Maximum6.972071
Range42.259446
Interquartile range (IQR)20.792428

Descriptive statistics

Standard deviation12.22558202
Coefficient of variation (CV)-0.8699537942
Kurtosis-1.198115564
Mean-14.05313949
Median Absolute Deviation (MAD)10.571317
Skewness-0.0153600043
Sum-4356.473241
Variance149.4648557
2020-08-01T13:43:29.591516image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-9.57166710.3%
 
-4.17940910.3%
 
-15.77438910.3%
 
-22.40365210.3%
 
-25.72313410.3%
 
-24.29419110.3%
 
-2.92558610.3%
 
-15.68230910.3%
 
-23.27911810.3%
 
-17.5208110.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
-35.28737510.3%
 
-35.07753710.3%
 
-34.92770910.3%
 
-34.8976610.3%
 
-34.72917310.3%
 
ValueCountFrequency (%) 
6.97207110.3%
 
6.86842310.3%
 
6.63505110.3%
 
6.57382910.3%
 
6.08956510.3%
 

scoliosis_slope
Real number (ℝ≥0)

UNIQUE

Distinct count310
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.64598064516129
Minimum7.0079
Maximum44.3412
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-01T13:43:29.919644image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7.0079
5-th percentile9.803365
Q117.189075
median24.93195
Q333.9796
95-th percentile42.67614
Maximum44.3412
Range37.3333
Interquartile range (IQR)16.790525

Descriptive statistics

Standard deviation10.45055818
Coefficient of variation (CV)0.4074930228
Kurtosis-1.141800639
Mean25.64598065
Median Absolute Deviation (MAD)8.3809
Skewness0.06421582241
Sum7950.254
Variance109.2141662
2020-08-01T13:43:30.254337image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
26.891710.3%
 
36.289910.3%
 
30.53310.3%
 
38.807110.3%
 
28.190210.3%
 
27.358710.3%
 
22.637510.3%
 
11.337510.3%
 
26.329710.3%
 
34.284610.3%
 
Other values (300)30096.8%
 
ValueCountFrequency (%) 
7.007910.3%
 
7.069810.3%
 
7.215710.3%
 
7.322210.3%
 
7.432410.3%
 
ValueCountFrequency (%) 
44.341210.3%
 
44.233810.3%
 
43.95510.3%
 
43.869310.3%
 
43.860810.3%
 

Status
Categorical

Distinct count2
Unique (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Abnormal
210
Normal
100
ValueCountFrequency (%) 
Abnormal21067.7%
 
Normal10032.3%
 
2020-08-01T13:43:30.816854image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.35483871
Min length6

Interactions

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Correlations

2020-08-01T13:43:31.238730image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-01T13:43:31.941833image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-01T13:43:32.644960image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-01T13:43:33.348085image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-01T13:43:20.582588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-01T13:43:21.723216image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

pelvic_incidencepelvic tiltlumbar_lordosis_anglesacral_slopepelvic_radiusdegree_spondylolisthesispelvic_slopeDirect_tiltthoracic_slopecervical_tiltsacrum_anglescoliosis_slopeStatus
063.02781822.55258639.60911740.47523298.672917-0.2544000.74450312.566114.538615.30468-28.65850143.5123Abnormal
139.05695110.06099125.01537828.995960114.4054254.5642590.41518612.887417.532316.78486-25.53060716.1102Abnormal
268.83202122.21848250.09219446.613539105.985135-3.5303170.47488926.834317.486116.65897-29.03188819.2221Abnormal
369.29700824.65287844.31123844.644130101.86849511.2115230.36934523.560312.707411.42447-30.47024618.8329Abnormal
449.7128599.65207528.31740640.060784108.1687257.9185010.54336035.494015.95468.87237-16.37837624.9171Abnormal
540.25020013.92190725.12495026.328293130.3278712.2306520.78999329.323012.003610.40462-1.5122099.6548Abnormal
653.43292815.86433637.16593437.568592120.5675235.9885510.19892013.851410.714611.37832-20.51043425.9477Abnormal
745.36675410.75561129.03834934.611142117.270068-10.6758710.13197328.81657.76767.60961-25.11145926.3543Abnormal
843.79019013.53375342.69081430.256437125.00289313.2890180.19040822.708511.423410.59188-20.02007540.0276Abnormal
936.6863535.01088441.94875131.67546984.2414150.6644370.36770026.20118.738014.91416-1.70209721.4320Abnormal

Last rows

pelvic_incidencepelvic tiltlumbar_lordosis_anglesacral_slopepelvic_radiusdegree_spondylolisthesispelvic_slopeDirect_tiltthoracic_slopecervical_tiltsacrum_anglescoliosis_slopeStatus
30050.6766776.46150135.00000044.215175116.587970-0.2147110.02117818.78468.00709.74352-1.22860414.2547Normal
30189.01487526.07598169.02125962.938894111.4810756.0615080.54450527.021913.373111.04819-3.50530033.4196Normal
30254.60031621.48897429.36021633.111342118.343321-1.4710670.96290730.855411.419813.82322-5.60644918.5514Normal
30334.3822992.06268332.39082032.319617128.300199-3.3655160.58116912.077416.62557.20496-31.37482329.5748Normal
30445.07545012.30695144.58317732.768499147.894637-8.9417090.93292232.116914.303710.64326-31.19884711.2307Normal
30547.90356513.61668836.00000034.286877117.449062-4.2453950.1297447.843314.74848.51707-15.72892711.5472Normal
30653.93674820.72149629.22053433.215251114.365845-0.4210100.04791319.198618.19727.087456.01384343.8693Normal
30761.44659722.69496846.17034738.751628125.670725-2.7078800.08107016.205913.55658.895723.56446318.4151Normal
30845.2527928.69315741.58312636.559635118.5458420.2147500.15925114.733416.09289.759225.76730833.7192Normal
30933.8416415.07399136.64123328.767649123.945244-0.1992490.67450419.382517.696313.729291.78300740.6049Normal